# coding=utf-

from PIL import Image, ImageFilter, ImageEnhance

from skimage.filters import threshold_otsu

import skimage.morphology as sm

from skimage.measure import regionprops

import matplotlib.pyplot as plt

import numpy as np

import pytesseract

import re

import os

import time

import logging

logging.basicConfig(level=logging.INFO, format="%(message)s", filename='train_output.log')

class ShopCert(object):

    def cut_region(self, img):

        """

        先按规则缩小搜索范围

        """

        w, h = img.size

        if h<:

            factor = max(, 1600.0/h)

            newsize = int(w*factor), int(h*factor)

            img = img.resize(newsize, Image.ANTIALIAS)

        if w<h:

            box = (w*0.4, h*0.18, w*0.96, h*0.6)

        else:

            box = (w*0.1, h*0.18, w*0.96, h*0.9)

        return img.crop(box)

    def detect_text(self, img):

        """

        检测字符区域

        """

        imgM = np.array(img.convert('L'))

        imgM =  * (imgM < threshold_otsu(imgM))

        imgM = sm.binary_closing(imgM, np.ones((, )))

        imgM = sm.remove_small_objects(imgM, )

        label_img = sm.label(imgM)

        imgList = []

        for region in regionprops(label_img):

            minr, minc, maxr, maxc = region.bbox

            w, h = (maxc-minc), (maxr-minr)

            if h > w * 0.2:

                continue

            box = minc-, minr-, maxc+, maxr+

            imgList.append(img.crop(box))

        return imgList

    def clear_noise(self, box):

        """

        降噪处理

        """

        box = box.convert('L')

 #       box = box.point(lambda x:  if x< else x)

        box = box.point(lambda x:  if x> else x)

        box = ImageEnhance.Contrast(box).enhance(2.5)

        return box

    def predict(self, fname, lang='eng'):

        """

        ocr 识别

        """

        img = Image.open(fname)

        # 先大致缩小范围

        region = self.cut_region(img)

        # 候选字符区域

#        region = self.clear_noise(region)

        boxList = self.detect_text(region)

        # 遍历识别

        for box in boxList:

            box = self.clear_noise(box)

            w, h = box.size

            if float(w)/h > 12.5:

                res = pytesseract.image_to_string(box, lang='chi_sim', config='-psm 7')

            else:

                res = pytesseract.image_to_string(box, lang='eng', config='-psm 7')

            res = re.sub('\s', '', res)  # 去除中间空白

            res = re.findall(r'[0-9][A-Z0-9]{13,20}', res)  # -20位

            for line in res:

                line = line.strip()

                if line.find(u'年')>:

                    continue

                print 'line', line

                if len(line)> :

                    box.save('img/clearNoise/%s_%s.jpg' % (fname.split('/')[-].split('.')[], line))

                    return line

                else:

                    print 'error line', line

        return 'error'

def show_pic(path='img/origin2/'):

    fnames = [os.path.join(path, fname) for fname in os.listdir(path)]

    for i, fname in enumerate(fnames, ):

        print fname

        img = Image.open(fname)

      #  img.save('./tesseract-train/cert.normal.exp%d.ttf' % i)

        img = ImageEnhance.Contrast(img).enhance(2.0)

        img = img.filter(ImageFilter.MedianFilter).convert('L')

        plt.figure(figsize=(, ), dpi=)

        plt.imshow(img, plt.cm.gray)

        plt.title(fname.split('/')[-]+'_%d' % i)

        plt.show()if __name__ == '__main__':

    test = ShopCert()

    path = 'img/origin2/'

    fnames = [os.path.join(path, fname) for fname in os.listdir(path) if fname.endswith('jpg')]

    fnames.sort()

    arguments = 'mode: L; enhance:2.0; h:0.5; dh:0.15'

    logging.info('%s' % arguments)

    logging.info("%s: %s" % ('imgname', 'result'))

    start_time = time.time()

    cnt =

    for idx, fname in enumerate(fnames, ):

        print idx, fname

        y_true = fname.split('/')[-].split('_')[]

        y_pred = test.predict(fname)

        if y_true == y_pred:

            cnt +=

            print fname

        else:

            print '***'*

            print 'error'

        logging.info("%s: %s" % (fname, y_pred))

        print 'y_true', y_true

        print 'y_pred', y_pred

        acc =  float(cnt)/idx

        print acc, cnt

        print '=='*, idx

        logging.info('%.3f %d/%d' % (acc, cnt, idx))

    print 'cost time: ', time.time()-start_time

    logging.info('accuracy: %.2f' % acc)